Overview

Dataset statistics

Number of variables22
Number of observations20116
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory176.0 B

Variable types

Numeric10
Text5
DateTime3
Categorical4

Alerts

city has constant value ""Constant
state has constant value ""Constant
address_number_start is highly overall correlated with address_number and 3 other fieldsHigh correlation
address_number is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
ward is highly overall correlated with police_districtHigh correlation
police_district is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
latitude is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
longitude is highly overall correlated with address_number_start and 2 other fieldsHigh correlation
street_type is highly imbalanced (52.2%)Imbalance
permit_number has unique valuesUnique
address_number_start has 1239 (6.2%) zerosZeros
address_number has 1239 (6.2%) zerosZeros

Reproduction

Analysis started2023-11-01 23:48:09.888957
Analysis finished2023-11-01 23:48:31.097586
Duration21.21 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

permit_number
Real number (ℝ)

UNIQUE 

Distinct20116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1153930.9
Minimum1000571
Maximum1862206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:31.208468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000571
5-th percentile1024771
Q11074657.5
median1106724.5
Q31131947.2
95-th percentile1647862.5
Maximum1862206
Range861635
Interquartile range (IQR)57289.75

Descriptive statistics

Standard deviation180136.64
Coefficient of variation (CV)0.15610696
Kurtosis5.2604434
Mean1153930.9
Median Absolute Deviation (MAD)25653
Skewness2.5378187
Sum2.3212474 × 1010
Variance3.244921 × 1010
MonotonicityNot monotonic
2023-11-01T23:48:31.428246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1556602 1
 
< 0.1%
1087124 1
 
< 0.1%
1087156 1
 
< 0.1%
1087150 1
 
< 0.1%
1087149 1
 
< 0.1%
1087146 1
 
< 0.1%
1087144 1
 
< 0.1%
1087143 1
 
< 0.1%
1087133 1
 
< 0.1%
1087122 1
 
< 0.1%
Other values (20106) 20106
> 99.9%
ValueCountFrequency (%)
1000571 1
< 0.1%
1001307 1
< 0.1%
1002652 1
< 0.1%
1002993 1
< 0.1%
1003612 1
< 0.1%
1004393 1
< 0.1%
1007248 1
< 0.1%
1007265 1
< 0.1%
1007306 1
< 0.1%
1007406 1
< 0.1%
ValueCountFrequency (%)
1862206 1
< 0.1%
1860048 1
< 0.1%
1855208 1
< 0.1%
1854101 1
< 0.1%
1852797 1
< 0.1%
1848630 1
< 0.1%
1848204 1
< 0.1%
1846561 1
< 0.1%
1845400 1
< 0.1%
1844680 1
< 0.1%

account_number
Real number (ℝ)

Distinct3017
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205950.64
Minimum12
Maximum495456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:31.679740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile5350
Q123077
median254059
Q3348401
95-th percentile414151
Maximum495456
Range495444
Interquartile range (IQR)325324

Descriptive statistics

Standard deviation156278.37
Coefficient of variation (CV)0.75881471
Kurtosis-1.6005292
Mean205950.64
Median Absolute Deviation (MAD)144576
Skewness-0.0770493
Sum4.142903 × 109
Variance2.442293 × 1010
MonotonicityNot monotonic
2023-11-01T23:48:31.897489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63414 886
 
4.4%
65004 312
 
1.6%
298727 114
 
0.6%
50161 85
 
0.4%
22633 66
 
0.3%
369504 64
 
0.3%
230211 61
 
0.3%
267891 48
 
0.2%
66022 45
 
0.2%
17658 44
 
0.2%
Other values (3007) 18391
91.4%
ValueCountFrequency (%)
12 6
 
< 0.1%
13 21
0.1%
16 4
 
< 0.1%
27 4
 
< 0.1%
28 11
0.1%
46 18
0.1%
51 6
 
< 0.1%
67 20
0.1%
73 17
0.1%
82 2
 
< 0.1%
ValueCountFrequency (%)
495456 1
< 0.1%
494737 1
< 0.1%
494267 1
< 0.1%
493688 1
< 0.1%
493621 1
< 0.1%
493348 1
< 0.1%
493334 1
< 0.1%
493192 1
< 0.1%
493107 1
< 0.1%
493069 1
< 0.1%

site_number
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3763671
Minimum1
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:32.082200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile23
Maximum230
Range229
Interquartile range (IQR)1

Descriptive statistics

Standard deviation18.124777
Coefficient of variation (CV)3.3711942
Kurtosis41.239485
Mean5.3763671
Median Absolute Deviation (MAD)0
Skewness5.9841774
Sum108151
Variance328.50756
MonotonicityNot monotonic
2023-11-01T23:48:32.293504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14713
73.1%
2 2297
 
11.4%
3 608
 
3.0%
4 287
 
1.4%
5 210
 
1.0%
7 121
 
0.6%
6 106
 
0.5%
11 98
 
0.5%
12 94
 
0.5%
19 91
 
0.5%
Other values (90) 1491
 
7.4%
ValueCountFrequency (%)
1 14713
73.1%
2 2297
 
11.4%
3 608
 
3.0%
4 287
 
1.4%
5 210
 
1.0%
6 106
 
0.5%
7 121
 
0.6%
8 65
 
0.3%
9 63
 
0.3%
10 67
 
0.3%
ValueCountFrequency (%)
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
221 1
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
211 2
< 0.1%
Distinct3025
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:32.690361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length67
Median length46
Mean length21.100418
Min length4

Characters and Unicode

Total characters424456
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique680 ?
Unique (%)3.4%

Sample

1st rowTHE LIFEWAY KEFIR SHOP LLC
2nd rowSQUARE KITCHEN, LLC
3rd rowGreek Prime Inc
4th rowGOMEZ RESTAURANT LLC
5th rowPLEASANT PIZZA, L.L.C.
ValueCountFrequency (%)
inc 8857
 
12.9%
llc 6126
 
8.9%
corporation 1580
 
2.3%
restaurant 1296
 
1.9%
1263
 
1.8%
starbucks 886
 
1.3%
chicago 882
 
1.3%
the 862
 
1.3%
corp 859
 
1.2%
cafe 705
 
1.0%
Other values (3620) 45488
66.1%
2023-11-01T23:48:33.358106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48844
 
11.5%
C 30246
 
7.1%
I 29081
 
6.9%
A 28335
 
6.7%
N 27602
 
6.5%
L 26454
 
6.2%
O 25669
 
6.0%
R 25232
 
5.9%
E 24954
 
5.9%
T 20570
 
4.8%
Other values (68) 137469
32.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 333477
78.6%
Space Separator 48844
 
11.5%
Other Punctuation 26493
 
6.2%
Lowercase Letter 7883
 
1.9%
Decimal Number 6929
 
1.6%
Dash Punctuation 645
 
0.2%
Open Punctuation 85
 
< 0.1%
Close Punctuation 85
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 30246
9.1%
I 29081
 
8.7%
A 28335
 
8.5%
N 27602
 
8.3%
L 26454
 
7.9%
O 25669
 
7.7%
R 25232
 
7.6%
E 24954
 
7.5%
T 20570
 
6.2%
S 20233
 
6.1%
Other values (16) 75101
22.5%
Lowercase Letter
ValueCountFrequency (%)
a 909
11.5%
e 905
11.5%
n 778
9.9%
o 653
8.3%
t 602
 
7.6%
r 591
 
7.5%
i 549
 
7.0%
s 513
 
6.5%
c 435
 
5.5%
l 353
 
4.5%
Other values (16) 1595
20.2%
Other Punctuation
ValueCountFrequency (%)
. 11715
44.2%
, 11205
42.3%
' 2277
 
8.6%
& 1097
 
4.1%
# 92
 
0.3%
/ 79
 
0.3%
" 18
 
0.1%
! 6
 
< 0.1%
@ 2
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1325
19.1%
2 982
14.2%
0 893
12.9%
3 796
11.5%
5 795
11.5%
4 709
10.2%
8 465
 
6.7%
6 364
 
5.3%
7 356
 
5.1%
9 244
 
3.5%
Space Separator
ValueCountFrequency (%)
48844
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 645
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 341360
80.4%
Common 83096
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 30246
 
8.9%
I 29081
 
8.5%
A 28335
 
8.3%
N 27602
 
8.1%
L 26454
 
7.7%
O 25669
 
7.5%
R 25232
 
7.4%
E 24954
 
7.3%
T 20570
 
6.0%
S 20233
 
5.9%
Other values (42) 82984
24.3%
Common
ValueCountFrequency (%)
48844
58.8%
. 11715
 
14.1%
, 11205
 
13.5%
' 2277
 
2.7%
1 1325
 
1.6%
& 1097
 
1.3%
2 982
 
1.2%
0 893
 
1.1%
3 796
 
1.0%
5 795
 
1.0%
Other values (16) 3167
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 424456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48844
 
11.5%
C 30246
 
7.1%
I 29081
 
6.9%
A 28335
 
6.7%
N 27602
 
6.5%
L 26454
 
6.2%
O 25669
 
6.0%
R 25232
 
5.9%
E 24954
 
5.9%
T 20570
 
4.8%
Other values (68) 137469
32.4%
Distinct3092
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:33.848209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length88
Median length45
Mean length16.847882
Min length1

Characters and Unicode

Total characters338912
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique669 ?
Unique (%)3.3%

Sample

1st rowLIFEWAY KEFIR SHOP
2nd rowFORK
3rd rowGreek Prime
4th rowDON PEPE
5th rowBOB'S PIZZA
ValueCountFrequency (%)
2393
 
4.3%
cafe 1444
 
2.6%
the 1373
 
2.5%
coffee 1313
 
2.3%
restaurant 1269
 
2.3%
bar 1209
 
2.2%
grill 1046
 
1.9%
starbucks 904
 
1.6%
inc 729
 
1.3%
and 636
 
1.1%
Other values (3513) 43590
78.0%
2023-11-01T23:48:34.581017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35917
 
10.6%
A 27910
 
8.2%
E 25846
 
7.6%
R 20135
 
5.9%
O 19834
 
5.9%
S 19383
 
5.7%
I 17485
 
5.2%
T 17396
 
5.1%
N 16778
 
5.0%
C 14863
 
4.4%
Other values (67) 123365
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 268029
79.1%
Space Separator 35917
 
10.6%
Lowercase Letter 18888
 
5.6%
Other Punctuation 9372
 
2.8%
Decimal Number 6183
 
1.8%
Dash Punctuation 477
 
0.1%
Math Symbol 42
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27910
 
10.4%
E 25846
 
9.6%
R 20135
 
7.5%
O 19834
 
7.4%
S 19383
 
7.2%
I 17485
 
6.5%
T 17396
 
6.5%
N 16778
 
6.3%
C 14863
 
5.5%
L 14449
 
5.4%
Other values (16) 73950
27.6%
Lowercase Letter
ValueCountFrequency (%)
e 2391
12.7%
a 2308
12.2%
o 1514
 
8.0%
n 1499
 
7.9%
r 1377
 
7.3%
i 1364
 
7.2%
t 1155
 
6.1%
l 1110
 
5.9%
s 1080
 
5.7%
u 717
 
3.8%
Other values (15) 4373
23.2%
Other Punctuation
ValueCountFrequency (%)
' 4093
43.7%
& 1842
19.7%
# 1296
 
13.8%
. 890
 
9.5%
, 579
 
6.2%
/ 572
 
6.1%
" 46
 
0.5%
! 43
 
0.5%
@ 8
 
0.1%
; 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1496
24.2%
1 936
15.1%
4 653
10.6%
3 600
9.7%
5 597
 
9.7%
0 569
 
9.2%
7 390
 
6.3%
6 361
 
5.8%
9 293
 
4.7%
8 288
 
4.7%
Space Separator
ValueCountFrequency (%)
35917
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 477
100.0%
Math Symbol
ValueCountFrequency (%)
+ 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 286917
84.7%
Common 51995
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27910
 
9.7%
E 25846
 
9.0%
R 20135
 
7.0%
O 19834
 
6.9%
S 19383
 
6.8%
I 17485
 
6.1%
T 17396
 
6.1%
N 16778
 
5.8%
C 14863
 
5.2%
L 14449
 
5.0%
Other values (41) 92838
32.4%
Common
ValueCountFrequency (%)
35917
69.1%
' 4093
 
7.9%
& 1842
 
3.5%
2 1496
 
2.9%
# 1296
 
2.5%
1 936
 
1.8%
. 890
 
1.7%
4 653
 
1.3%
3 600
 
1.2%
5 597
 
1.1%
Other values (16) 3675
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 338912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35917
 
10.6%
A 27910
 
8.2%
E 25846
 
7.6%
R 20135
 
5.9%
O 19834
 
5.9%
S 19383
 
5.7%
I 17485
 
5.2%
T 17396
 
5.1%
N 16778
 
5.0%
C 14863
 
4.4%
Other values (67) 123365
36.4%
Distinct2997
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-03-14 00:00:00
Maximum2023-10-18 00:00:00
2023-11-01T23:48:34.847938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:35.178470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-11-01 00:00:00
Maximum2024-02-29 00:00:00
2023-11-01T23:48:35.713769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:35.966484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
Distinct2946
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-03-14 00:00:00
Maximum2023-10-18 00:00:00
2023-11-01T23:48:36.181737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:36.366112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2575
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:36.726547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length23
Mean length16.682541
Min length10

Characters and Unicode

Total characters335586
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique446 ?
Unique (%)2.2%

Sample

1st row0 W DIVISION ST
2nd row4600 N LINCOLN AVE
3rd row0 W 35TH ST
4th row3616 W 26TH ST
5th row1659 W 21ST ST
ValueCountFrequency (%)
st 10459
 
12.9%
n 9115
 
11.2%
ave 8057
 
9.9%
w 7263
 
8.9%
e 1879
 
2.3%
s 1859
 
2.3%
0 1239
 
1.5%
clark 1189
 
1.5%
wells 1139
 
1.4%
lincoln 1043
 
1.3%
Other values (1881) 37932
46.7%
2023-11-01T23:48:37.471414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61058
18.2%
E 21334
 
6.4%
S 21161
 
6.3%
A 20969
 
6.2%
N 20512
 
6.1%
T 16685
 
5.0%
1 12447
 
3.7%
L 12242
 
3.6%
I 11531
 
3.4%
W 11412
 
3.4%
Other values (26) 126235
37.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 206343
61.5%
Decimal Number 68185
 
20.3%
Space Separator 61058
 
18.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 21334
10.3%
S 21161
10.3%
A 20969
10.2%
N 20512
9.9%
T 16685
 
8.1%
L 12242
 
5.9%
I 11531
 
5.6%
W 11412
 
5.5%
R 10946
 
5.3%
O 10502
 
5.1%
Other values (15) 49049
23.8%
Decimal Number
ValueCountFrequency (%)
1 12447
18.3%
0 10235
15.0%
2 8735
12.8%
3 8200
12.0%
5 7626
11.2%
4 6592
9.7%
6 4474
 
6.6%
7 4027
 
5.9%
8 3100
 
4.5%
9 2749
 
4.0%
Space Separator
ValueCountFrequency (%)
61058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206343
61.5%
Common 129243
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 21334
10.3%
S 21161
10.3%
A 20969
10.2%
N 20512
9.9%
T 16685
 
8.1%
L 12242
 
5.9%
I 11531
 
5.6%
W 11412
 
5.5%
R 10946
 
5.3%
O 10502
 
5.1%
Other values (15) 49049
23.8%
Common
ValueCountFrequency (%)
61058
47.2%
1 12447
 
9.6%
0 10235
 
7.9%
2 8735
 
6.8%
3 8200
 
6.3%
5 7626
 
5.9%
4 6592
 
5.1%
6 4474
 
3.5%
7 4027
 
3.1%
8 3100
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61058
18.2%
E 21334
 
6.4%
S 21161
 
6.3%
A 20969
 
6.2%
N 20512
 
6.1%
T 16685
 
5.0%
1 12447
 
3.7%
L 12242
 
3.6%
I 11531
 
3.4%
W 11412
 
3.4%
Other values (26) 126235
37.6%

address_number_start
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1640
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1664.0896
Minimum0
Maximum11208
Zeros1239
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:37.704635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1235.5
Q32529
95-th percentile4857
Maximum11208
Range11208
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation1628.364
Coefficient of variation (CV)0.97853146
Kurtosis1.3921521
Mean1664.0896
Median Absolute Deviation (MAD)1033.5
Skewness1.220232
Sum33474826
Variance2651569.3
MonotonicityNot monotonic
2023-11-01T23:48:37.897593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1239
 
6.2%
200 152
 
0.8%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.5%
20 91
 
0.5%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
2200 75
 
0.4%
Other values (1630) 17997
89.5%
ValueCountFrequency (%)
0 1239
6.2%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

address_number
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1640
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1664.0896
Minimum0
Maximum11208
Zeros1239
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:38.151124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1235.5
Q32529
95-th percentile4857
Maximum11208
Range11208
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation1628.364
Coefficient of variation (CV)0.97853146
Kurtosis1.3921521
Mean1664.0896
Median Absolute Deviation (MAD)1033.5
Skewness1.220232
Sum33474826
Variance2651569.3
MonotonicityNot monotonic
2023-11-01T23:48:38.360021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1239
 
6.2%
200 152
 
0.8%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.5%
20 91
 
0.5%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
2200 75
 
0.4%
Other values (1630) 17997
89.5%
ValueCountFrequency (%)
0 1239
6.2%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

street_direction
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
N
9115 
W
7263 
E
1879 
S
1859 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20116
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowN
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
N 9115
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Length

2023-11-01T23:48:38.585750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T23:48:38.732277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 9115
45.3%
w 7263
36.1%
e 1879
 
9.3%
s 1859
 
9.2%

Most occurring characters

ValueCountFrequency (%)
N 9115
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20116
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9115
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 20116
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9115
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9115
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

street
Text

Distinct228
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:39.083660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length13
Mean length6.8899384
Min length3

Characters and Unicode

Total characters138598
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.1%

Sample

1st rowDIVISION
2nd rowLINCOLN
3rd row35TH
4th row26TH
5th row21ST
ValueCountFrequency (%)
clark 1189
 
5.7%
wells 1139
 
5.5%
lincoln 1043
 
5.0%
division 940
 
4.5%
michigan 729
 
3.5%
randolph 656
 
3.1%
southport 617
 
3.0%
milwaukee 599
 
2.9%
state 536
 
2.6%
halsted 501
 
2.4%
Other values (230) 12877
61.8%
2023-11-01T23:48:39.738634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 12912
 
9.3%
L 11718
 
8.5%
I 11531
 
8.3%
E 11398
 
8.2%
N 11397
 
8.2%
O 10502
 
7.6%
R 10142
 
7.3%
S 8883
 
6.4%
T 6194
 
4.5%
D 6012
 
4.3%
Other values (25) 37909
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 136961
98.8%
Decimal Number 927
 
0.7%
Space Separator 710
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12912
 
9.4%
L 11718
 
8.6%
I 11531
 
8.4%
E 11398
 
8.3%
N 11397
 
8.3%
O 10502
 
7.7%
R 10142
 
7.4%
S 8883
 
6.5%
T 6194
 
4.5%
D 6012
 
4.4%
Other values (15) 36272
26.5%
Decimal Number
ValueCountFrequency (%)
3 310
33.4%
5 226
24.4%
1 143
15.4%
8 60
 
6.5%
6 56
 
6.0%
7 49
 
5.3%
2 44
 
4.7%
9 26
 
2.8%
4 13
 
1.4%
Space Separator
ValueCountFrequency (%)
710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136961
98.8%
Common 1637
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12912
 
9.4%
L 11718
 
8.6%
I 11531
 
8.4%
E 11398
 
8.3%
N 11397
 
8.3%
O 10502
 
7.7%
R 10142
 
7.4%
S 8883
 
6.5%
T 6194
 
4.5%
D 6012
 
4.4%
Other values (15) 36272
26.5%
Common
ValueCountFrequency (%)
710
43.4%
3 310
18.9%
5 226
 
13.8%
1 143
 
8.7%
8 60
 
3.7%
6 56
 
3.4%
7 49
 
3.0%
2 44
 
2.7%
9 26
 
1.6%
4 13
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12912
 
9.3%
L 11718
 
8.5%
I 11531
 
8.3%
E 11398
 
8.2%
N 11397
 
8.2%
O 10502
 
7.6%
R 10142
 
7.3%
S 8883
 
6.4%
T 6194
 
4.5%
D 6012
 
4.3%
Other values (25) 37909
27.4%

street_type
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
ST
10419 
AVE
8057 
RD
 
608
BLVD
 
269
PL
 
255
Other values (4)
 
508

Length

Max length4
Median length2
Mean length2.4490952
Min length2

Characters and Unicode

Total characters49266
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowAVE
3rd rowST
4th rowST
5th rowST

Common Values

ValueCountFrequency (%)
ST 10419
51.8%
AVE 8057
40.1%
RD 608
 
3.0%
BLVD 269
 
1.3%
PL 255
 
1.3%
PKWY 199
 
1.0%
DR 196
 
1.0%
CT 72
 
0.4%
HWY 41
 
0.2%

Length

2023-11-01T23:48:39.976270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T23:48:40.211768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
st 10419
51.8%
ave 8057
40.1%
rd 608
 
3.0%
blvd 269
 
1.3%
pl 255
 
1.3%
pkwy 199
 
1.0%
dr 196
 
1.0%
ct 72
 
0.4%
hwy 41
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 10491
21.3%
S 10419
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49266
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10491
21.3%
S 10419
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 49266
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10491
21.3%
S 10419
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10491
21.3%
S 10419
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

city
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
CHICAGO
20116 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140812
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHICAGO
2nd rowCHICAGO
3rd rowCHICAGO
4th rowCHICAGO
5th rowCHICAGO

Common Values

ValueCountFrequency (%)
CHICAGO 20116
100.0%

Length

2023-11-01T23:48:40.410474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T23:48:40.531200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
chicago 20116
100.0%

Most occurring characters

ValueCountFrequency (%)
C 40232
28.6%
H 20116
14.3%
I 20116
14.3%
A 20116
14.3%
G 20116
14.3%
O 20116
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 140812
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 40232
28.6%
H 20116
14.3%
I 20116
14.3%
A 20116
14.3%
G 20116
14.3%
O 20116
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 140812
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 40232
28.6%
H 20116
14.3%
I 20116
14.3%
A 20116
14.3%
G 20116
14.3%
O 20116
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 40232
28.6%
H 20116
14.3%
I 20116
14.3%
A 20116
14.3%
G 20116
14.3%
O 20116
14.3%

state
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
IL
20116 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters40232
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL 20116
100.0%

Length

2023-11-01T23:48:40.675232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T23:48:40.806249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
il 20116
100.0%

Most occurring characters

ValueCountFrequency (%)
I 20116
50.0%
L 20116
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40232
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 20116
50.0%
L 20116
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40232
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 20116
50.0%
L 20116
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 20116
50.0%
L 20116
50.0%

zip_code
Real number (ℝ)

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60625.512
Minimum60601
Maximum60707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:41.025680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60601
5-th percentile60602
Q160610
median60618
Q360647
95-th percentile60657
Maximum60707
Range106
Interquartile range (IQR)37

Descriptive statistics

Standard deviation19.888896
Coefficient of variation (CV)0.0003280615
Kurtosis-0.68234855
Mean60625.512
Median Absolute Deviation (MAD)11
Skewness0.70032352
Sum1.2195428 × 109
Variance395.56819
MonotonicityNot monotonic
2023-11-01T23:48:41.238722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60611 1840
 
9.1%
60654 1768
 
8.8%
60622 1723
 
8.6%
60614 1551
 
7.7%
60657 1420
 
7.1%
60607 1156
 
5.7%
60613 981
 
4.9%
60610 948
 
4.7%
60647 883
 
4.4%
60618 835
 
4.2%
Other values (43) 7011
34.9%
ValueCountFrequency (%)
60601 652
3.2%
60602 410
 
2.0%
60603 330
 
1.6%
60604 234
 
1.2%
60605 729
3.6%
60606 385
 
1.9%
60607 1156
5.7%
60608 207
 
1.0%
60609 31
 
0.2%
60610 948
4.7%
ValueCountFrequency (%)
60707 47
 
0.2%
60661 504
 
2.5%
60660 199
 
1.0%
60659 189
 
0.9%
60657 1420
7.1%
60656 15
 
0.1%
60655 3
 
< 0.1%
60654 1768
8.8%
60653 20
 
0.1%
60651 3
 
< 0.1%

ward
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.484192
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:41.434934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q126
median42
Q343
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.404843
Coefficient of variation (CV)0.52105015
Kurtosis-0.67601154
Mean31.484192
Median Absolute Deviation (MAD)5
Skewness-0.9521053
Sum633336
Variance269.11887
MonotonicityNot monotonic
2023-11-01T23:48:41.680311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
42 5554
27.6%
1 1579
 
7.8%
27 1537
 
7.6%
47 1476
 
7.3%
44 1427
 
7.1%
2 1374
 
6.8%
43 1187
 
5.9%
32 1114
 
5.5%
4 666
 
3.3%
25 471
 
2.3%
Other values (34) 3731
18.5%
ValueCountFrequency (%)
1 1579
7.8%
2 1374
6.8%
3 289
 
1.4%
4 666
3.3%
5 103
 
0.5%
6 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 13
 
0.1%
11 331
 
1.6%
ValueCountFrequency (%)
50 104
 
0.5%
49 148
 
0.7%
48 361
 
1.8%
47 1476
 
7.3%
46 335
 
1.7%
45 238
 
1.2%
44 1427
 
7.1%
43 1187
 
5.9%
42 5554
27.6%
41 81
 
0.4%

police_district
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.841072
Minimum0
Maximum25
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:41.912871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median18
Q319
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.8661682
Coefficient of variation (CV)0.496072
Kurtosis-0.40058466
Mean13.841072
Median Absolute Deviation (MAD)2
Skewness-1.0016119
Sum278427
Variance47.144266
MonotonicityNot monotonic
2023-11-01T23:48:42.106424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 5337
26.5%
19 4255
21.2%
1 3656
18.2%
12 2388
11.9%
14 1788
 
8.9%
20 760
 
3.8%
16 418
 
2.1%
17 373
 
1.9%
24 309
 
1.5%
2 237
 
1.2%
Other values (13) 595
 
3.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3656
18.2%
2 237
 
1.2%
3 10
 
< 0.1%
4 15
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 44
 
0.2%
9 223
 
1.1%
ValueCountFrequency (%)
25 149
 
0.7%
24 309
 
1.5%
22 27
 
0.1%
20 760
 
3.8%
19 4255
21.2%
18 5337
26.5%
17 373
 
1.9%
16 418
 
2.1%
15 7
 
< 0.1%
14 1788
 
8.9%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2567
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.910374
Minimum41.69067
Maximum42.019421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:42.357993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41.69067
5-th percentile41.865345
Q141.885257
median41.902442
Q341.939257
95-th percentile41.978062
Maximum42.019421
Range0.32875146
Interquartile range (IQR)0.053999362

Descriptive statistics

Standard deviation0.037932607
Coefficient of variation (CV)0.00090508872
Kurtosis1.7970222
Mean41.910374
Median Absolute Deviation (MAD)0.020466559
Skewness-0.10651859
Sum843069.08
Variance0.0014388826
MonotonicityNot monotonic
2023-11-01T23:48:42.606931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88200199 98
 
0.5%
41.88197573 96
 
0.5%
41.90405052 85
 
0.4%
41.87801449 81
 
0.4%
41.88460018 69
 
0.3%
41.8825402 53
 
0.3%
41.90186736 40
 
0.2%
41.89678605 40
 
0.2%
41.88216417 37
 
0.2%
41.87949547 37
 
0.2%
Other values (2557) 19480
96.8%
ValueCountFrequency (%)
41.69066951 1
 
< 0.1%
41.69139989 2
 
< 0.1%
41.69245222 1
 
< 0.1%
41.69920305 10
< 0.1%
41.70289718 1
 
< 0.1%
41.70356373 2
 
< 0.1%
41.71874411 1
 
< 0.1%
41.72107515 10
< 0.1%
41.72112515 1
 
< 0.1%
41.72177014 1
 
< 0.1%
ValueCountFrequency (%)
42.01942097 12
0.1%
42.0193885 5
 
< 0.1%
42.01934594 4
 
< 0.1%
42.01933013 3
 
< 0.1%
42.01932963 2
 
< 0.1%
42.0193098 4
 
< 0.1%
42.01927235 1
 
< 0.1%
42.0174068 8
< 0.1%
42.01615704 15
0.1%
42.01615267 15
0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2567
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.6553
Minimum-87.834308
Maximum-87.535139
Zeros0
Zeros (%)0.0%
Negative20116
Negative (%)100.0%
Memory size157.3 KiB
2023-11-01T23:48:42.909848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-87.834308
5-th percentile-87.709805
Q1-87.672586
median-87.648962
Q3-87.629547
95-th percentile-87.624223
Maximum-87.535139
Range0.29916895
Interquartile range (IQR)0.043038819

Descriptive statistics

Standard deviation0.03338854
Coefficient of variation (CV)-0.00038090726
Kurtosis5.12465
Mean-87.6553
Median Absolute Deviation (MAD)0.020372619
Skewness-1.7591978
Sum-1763274
Variance0.0011147946
MonotonicityNot monotonic
2023-11-01T23:48:43.165742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.63103164 98
 
0.5%
-87.63397185 96
 
0.5%
-87.62874675 85
 
0.4%
-87.63318903 81
 
0.4%
-87.62798897 69
 
0.3%
-87.62453095 53
 
0.3%
-87.62849214 40
 
0.2%
-87.62828088 40
 
0.2%
-87.62451427 37
 
0.2%
-87.63382966 37
 
0.2%
Other values (2557) 19480
96.8%
ValueCountFrequency (%)
-87.8343079 1
 
< 0.1%
-87.82625503 9
< 0.1%
-87.82618448 1
 
< 0.1%
-87.82167425 5
 
< 0.1%
-87.82042719 4
 
< 0.1%
-87.81865423 16
0.1%
-87.81795264 4
 
< 0.1%
-87.81783297 3
 
< 0.1%
-87.81729036 11
0.1%
-87.8172596 1
 
< 0.1%
ValueCountFrequency (%)
-87.53513895 2
 
< 0.1%
-87.55117213 1
 
< 0.1%
-87.55124869 1
 
< 0.1%
-87.55161886 9
< 0.1%
-87.56729719 2
 
< 0.1%
-87.58184369 4
< 0.1%
-87.58390766 1
 
< 0.1%
-87.58502961 2
 
< 0.1%
-87.58781452 8
< 0.1%
-87.58797399 7
< 0.1%
Distinct2567
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-01T23:48:43.571158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length40
Median length39
Mean length39.105538
Min length35

Characters and Unicode

Total characters786647
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445 ?
Unique (%)2.2%

Sample

1st row(41.90405051948726, -87.62874675447662)
2nd row(41.964902360748326, -87.68627917084095)
3rd row(41.831121574889146, -87.62670680090584)
4th row(41.844483527070835, -87.71558453559561)
5th row(41.853999857174315, -87.66845450091006)
ValueCountFrequency (%)
41.88200198545344 98
 
0.2%
87.6310316367502 98
 
0.2%
41.881975727713886 96
 
0.2%
87.63397184627037 96
 
0.2%
41.90405051948726 85
 
0.2%
87.62874675447662 85
 
0.2%
41.878014487249544 81
 
0.2%
87.63318903001444 81
 
0.2%
87.62798896732363 69
 
0.2%
41.884600177780484 69
 
0.2%
Other values (5124) 39374
97.9%
2023-11-01T23:48:44.158226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 84465
10.7%
4 75171
9.6%
7 74161
9.4%
6 72016
9.2%
1 70017
8.9%
9 62712
8.0%
2 53703
 
6.8%
3 53206
 
6.8%
5 52647
 
6.7%
0 47737
 
6.1%
Other values (6) 140812
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 645835
82.1%
Other Punctuation 60348
 
7.7%
Open Punctuation 20116
 
2.6%
Space Separator 20116
 
2.6%
Dash Punctuation 20116
 
2.6%
Close Punctuation 20116
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 84465
13.1%
4 75171
11.6%
7 74161
11.5%
6 72016
11.2%
1 70017
10.8%
9 62712
9.7%
2 53703
8.3%
3 53206
8.2%
5 52647
8.2%
0 47737
7.4%
Other Punctuation
ValueCountFrequency (%)
. 40232
66.7%
, 20116
33.3%
Open Punctuation
ValueCountFrequency (%)
( 20116
100.0%
Space Separator
ValueCountFrequency (%)
20116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20116
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 786647
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 84465
10.7%
4 75171
9.6%
7 74161
9.4%
6 72016
9.2%
1 70017
8.9%
9 62712
8.0%
2 53703
 
6.8%
3 53206
 
6.8%
5 52647
 
6.7%
0 47737
 
6.1%
Other values (6) 140812
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 786647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 84465
10.7%
4 75171
9.6%
7 74161
9.4%
6 72016
9.2%
1 70017
8.9%
9 62712
8.0%
2 53703
 
6.8%
3 53206
 
6.8%
5 52647
 
6.7%
0 47737
 
6.1%
Other values (6) 140812
17.9%

Interactions

2023-11-01T23:48:28.660919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:12.493747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:14.068144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:15.795102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.373187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.235030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:20.917391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:22.763081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:24.729486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:26.940291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:28.802122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:12.655957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:14.200554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:15.946238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.514996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.370938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:21.173619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:22.931867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:24.989545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:27.150993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:28.964016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:12.810441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:14.378414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:16.091996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.669610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.503332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:21.363384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:23.144250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:25.291790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:27.348288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:29.141554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:12.947597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:14.577679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:16.216006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.825943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.635840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:21.514340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:23.335853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:25.568723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:27.505011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:29.299082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:13.106272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:14.749178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:16.359191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.974249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.807359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:21.681956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:23.497127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:25.866685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:27.659922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:29.493548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:13.267756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:14.893586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:16.498551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:18.382754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.931621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:21.843529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:23.713227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:26.049865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:27.809384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:29.640295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:13.448419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:15.071829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:16.673548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:18.518198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:20.107744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:22.039983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:23.931824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:26.273925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:27.956048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:29.833239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:13.618861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:15.237029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:16.830655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:18.702170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:20.235451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:22.258052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:24.093340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:26.495454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:28.116570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:29.973110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:13.778869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:15.455317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.016237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:18.880074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:20.459572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:22.401353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:24.305423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:26.622604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:28.338510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:30.121900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:13.938088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:15.614682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:17.205270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:19.069224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:20.651365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:22.611564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:24.560881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:26.784787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T23:48:28.525438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-01T23:48:44.322457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
permit_numberaccount_numbersite_numberaddress_number_startaddress_numberzip_codewardpolice_districtlatitudelongitudestreet_directionstreet_type
permit_number1.0000.4730.017-0.174-0.174-0.018-0.082-0.156-0.1430.0390.0370.041
account_number0.4731.000-0.174-0.123-0.123-0.046-0.108-0.158-0.1360.0210.0750.067
site_number0.017-0.1741.000-0.122-0.122-0.0970.075-0.051-0.0740.1440.0690.054
address_number_start-0.174-0.123-0.1221.0001.0000.3780.1760.5130.707-0.7440.2940.210
address_number-0.174-0.123-0.1221.0001.0000.3780.1760.5130.707-0.7440.2940.210
zip_code-0.018-0.046-0.0970.3780.3781.0000.1770.4520.450-0.4760.3070.222
ward-0.082-0.1080.0750.1760.1760.1771.0000.5100.453-0.0490.2860.167
police_district-0.156-0.158-0.0510.5130.5130.4520.5101.0000.813-0.3390.3830.186
latitude-0.143-0.136-0.0740.7070.7070.4500.4530.8131.000-0.6330.3850.212
longitude0.0390.0210.144-0.744-0.744-0.476-0.049-0.339-0.6331.0000.3210.268
street_direction0.0370.0750.0690.2940.2940.3070.2860.3830.3850.3211.0000.241
street_type0.0410.0670.0540.2100.2100.2220.1670.1860.2120.2680.2411.000

Missing values

2023-11-01T23:48:30.387555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-01T23:48:30.859527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

permit_numberaccount_numbersite_numberlegal_namedoing_business_as_nameissued_dateexpiration_datepayment_dateaddressaddress_number_startaddress_numberstreet_directionstreetstreet_typecitystatezip_codewardpolice_districtlatitudelongitudelocation
015566023289921THE LIFEWAY KEFIR SHOP LLCLIFEWAY KEFIR SHOP07/16/202102/28/202207/16/20210 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.0118.041.904051-87.628747(41.90405051948726, -87.62874675447662)
115345562527421SQUARE KITCHEN, LLCFORK07/16/202102/28/202207/16/20214600 N LINCOLN AVE46004600NLINCOLNAVECHICAGOIL60625.04719.041.964902-87.686279(41.964902360748326, -87.68627917084095)
215362764751141Greek Prime IncGreek Prime07/19/202102/28/202207/19/20210 W 35TH ST00W35THSTCHICAGOIL60609.0112.041.831122-87.626707(41.831121574889146, -87.62670680090584)
315482403696671GOMEZ RESTAURANT LLCDON PEPE07/20/202102/28/202207/20/20213616 W 26TH ST36163616W26THSTCHICAGOIL60623.02210.041.844484-87.715585(41.844483527070835, -87.71558453559561)
415313184314221PLEASANT PIZZA, L.L.C.BOB'S PIZZA07/23/202102/28/202207/23/20211659 W 21ST ST16591659W21STSTCHICAGOIL60608.02512.041.854000-87.668455(41.853999857174315, -87.66845450091006)
515338424579921MIR - MUR, INC.,The Great American Bagel07/23/202102/28/202207/23/20211154 W MADISON ST11541154WMADISONSTCHICAGOIL60607.02512.041.881737-87.656547(41.88173703022226, -87.65654694665614)
615388983173831AJD RESTAURANT GROUP, LLCSULLY'S HOUSE07/23/202102/28/202207/22/20211501 N DAYTON ST15011501NDAYTONSTCHICAGOIL60642.0218.041.908620-87.649295(41.90861972732258, -87.64929457094323)
715338483983053ROANOKE HOSPITALITY, LLCROANOKE12/10/202102/28/202206/14/2021135 W MADISON ST135135WMADISONSTCHICAGOIL60602.0421.041.881857-87.631896(41.88185674363783, -87.63189616155178)
811483964697911EL CHUZO, INC.SAUGANASH GRILL12/13/202102/28/202204/19/20216005 N SAUGANASH AVE60056005NSAUGANASHAVECHICAGOIL60646.03917.041.990266-87.732997(41.990266341555525, -87.73299734383897)
915488492113241ITALIAN RISTORANTE-HUBBARD, LLCVERMILION12/18/202102/28/202212/18/202110 W HUBBARD ST1010WHUBBARDSTCHICAGOIL60654.04218.041.890169-87.628394(41.89016858549094, -87.62839433601951)
permit_numberaccount_numbersite_numberlegal_namedoing_business_as_nameissued_dateexpiration_datepayment_dateaddressaddress_number_startaddress_numberstreet_directionstreetstreet_typecitystatezip_codewardpolice_districtlatitudelongitudelocation
2010618133534936881THE UNDERSTUDY COFFEE AND BOOKS, LLCTHE UNDERSTUDY COFFEE AND BOOKS06/15/202302/29/202406/15/20235531 N CLARK ST55315531NCLARKSTCHICAGOIL60640.04820.041.982613-87.668426(41.982613033865064, -87.66842628885883)
201071826782158481FIGUEROA BAR, INC.DAMEN TAVERN06/13/202302/29/202406/13/20231958 W HURON ST19581958WHURONSTCHICAGOIL60622.03612.041.894205-87.676882(41.89420534591261, -87.67688170620728)
2010818286234785791CAFE SOPHIE GOLD COAST LLCCafe Sophie06/15/202302/29/202406/15/20230 N STATE ST00NSTATESTCHICAGOIL60610.0421.041.882083-87.627964(41.882082751743674, -87.62796364640772)
2010918056124930691BUCKTOWN BBQ, INC.FIREWOOD BBQ06/21/202302/29/202406/21/20231600 W NORTH AVE16001600WNORTHAVECHICAGOIL60622.03214.041.910739-87.667756(41.91073912357612, -87.66775554223523)
2011018236174685601BUREAU BAR AND RESTAURANT LLCBUREAU BAR AND RESTAURANT06/21/202302/29/202406/21/20230 S STATE ST00SSTATESTCHICAGOIL60616.031.041.881960-87.627937(41.881960316026536, -87.6279372789252)
2011118090454869151River North Egg Harbor, LLCEgg Harbor Cafe06/21/202302/29/202406/21/2023800 N WELLS ST800800NWELLSSTCHICAGOIL60610.02718.041.896634-87.634351(41.8966337490024, -87.63435133722824)
2011218160174902291LA ESQUINA DEL TACO INC.,LA ESQUINA DEL TACO06/21/202302/29/202406/21/20233259 W 63RD ST32593259W63RDSTCHICAGOIL60629.0148.041.778828-87.705434(41.77882757627881, -87.70543426650146)
2011318156702943291RICHMOND TAVERN, INC.RICHMOND TAVERN06/27/202302/29/202406/27/20232944 W GRAND AVE29442944WGRANDAVECHICAGOIL60622.03612.041.896089-87.700748(41.89608917059594, -87.70074823336402)
2011418154874866991CASA AMIGOS RESTAURANT BAR LLCLOS MOLCAJETES RESTAURANT BAR06/27/202302/29/202406/27/20233830 W LAWRENCE AVE38303830WLAWRENCEAVECHICAGOIL60625.03517.041.968390-87.724448(41.968390431264375, -87.72444785924317)
2011518347814631881ETTA RIVER NORTH, LLCETTA06/27/202302/29/202406/27/20230 N CLARK ST00NCLARKSTCHICAGOIL60654.0421.041.882002-87.631032(41.88200198545344, -87.6310316367502)